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NMR and Isotopic Fingerprinting
for Food Characterisation
2007 EUR 22724 EN
EUROPEAN COMMISSION
Joint Research CentreDIRECTORATE-GENERAL
Institute for Health and Consumer Protection
Authors
R. M. Alonso-Salces, J. M. Moreno-Rojas, V. M. Holland, F. Reniero, C. Guillou, F. Serra, N. Segebarth
European CommissionJoint Research Centre (DG JRC)Institute for Health and Consumer Protection (IHCP)Physical and Chemical Exposure Unit (PCE) / BEVABSVia Enrico Fermi, Bld. 28f, TP 281I-21020 Ispra (VA), Italy
Tel.: +0039 0332 78 6568Fax: +0039 0332 78 9453
Email: [email protected]: http://ihcp.jrc.cec.eu.int/
Authors: R. M. Alonso-Salces, J. M. Moreno-Rojas, V. M. Holland, F. Reniero, C. Guillou, F. Serra, N. SegebarthCover: José-Joaquín Blasco
Legal NoticeNeither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of the information contained in this production.
EUR 22724 EN
ISSN 1018-5593
ISBN 978-92-79-05309-2
© European Communities, 2007
Printed in Italy
OUR MISSION
The mission of the Joint Research Centre is to provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of European Union policies. As a service of the European Commission, the Joint Re-search Centre functions as a reference centre of science and technology for the Union. Close to the policy-making process, it serves the common interest of the Member States, while being independent of special interests, whether private or national.
NMR AND ISOTOPIC FINGERPRINTING FOR FOOD CHARACTERISATION
R.M. Alonso-Salces, J.M. Moreno-Rojas, V.M. Holland,
F. Reniero, C. Guillou, F. Serra, N. Segebarth
1. NMR and Isotopic fingerprinting
2. Applications in the food sector
2.1. Food authenticity and traceability
- Wild or farmed origin of salmon
- Wild or farmed origin of Gilthead Sea Bream (Sparus aurata)
- Polyunsaturated Fatty Acids in fish oils: Specie and farming origin
- Natural or synthetic origin of tartaric acid
2.2. Food process quality control
3. A case study: Characterization of PDO olive oils by NMR and IRMS 10
- Influence of the year of production on the PDO olive oils
- PDO olive oils classification by country
- Italian PDO olive oils classification by region
- Binary classification of olive oils related to the membership to a
certain PDO: “Rivera Ligure”
- 1H-NMR of the unsaponifiable fraction of olive oils for the
determination of geographical origin
Conclusions
Bibliography
1. NMR and Isotopic fingerprinting
Numerous analytical methods have been developed during the past decades
and have proven to be extremely efficient, for instance, in the case of single, high
purity compounds for the measurements of concentration and/or structure elucidation.
However, real-world applications often require the characterization of complex
mixtures containing tens to thousands of compounds, such as biofluids, food matrices,
industrial products, etc. The complete characterisation of such mixtures would be
tedious, not to say impossible in the case of mixtures containing hundreds of
compounds, and certainly unfeasible for monitoring purposes. In fact, one can
concentrate on one or a few molecules which entail the non-negligible issue of the
choice of the molecules of interest, and therefore require an a priori knowledge.
Nevertheless this approach usually requires molecular separation and purification,
which is time, money and human resource consuming.
In contrast the Nuclear Magnetic Resonance (NMR) fingerprinting aims at
establishing a holistic approach: the mixture is submitted to the NMR experiment as a
whole. A simple quantification of the major compounds, which are characterised by
one or several signals in the NMR spectrum, can be performed. This type of analysis
is particularly attractive for several reasons: it is non-destructive, non selective and
cost effective; requires little or no sample pre-treatment; uses small amounts of
organic solvents or reagents; and typically takes only a few minutes per sample.
The spectra of complex mixtures show hundreds of signals, coming from
numerous molecules. This and the overlap of signal make it difficult to extract
information, either “visually” or by simple processing of the data. The most effective
way to analyse these “holistic profiles” is by using chemometric tools which enable
the visualisation of the data in a reduced dimension and the classification of the
samples into established classes based on inherent patterns in a set of spectral
measurements. Moreover, these techniques also allow to trace the NMR spectral
variables responsible of this classification, and thus, identify molecular markers of
interest.
Isotopic measurements such as Isotopic Ratio Mass Spectroscopy (IRMS) or
Site-specific Natural Isotopic Fractionation (SNIF-NMR) provide few variables, but
these contain unique information on geographical origin and metabolic or production
pathways. Thus, isotopic measurements provide complementary data to NMR
fingerprinting.
2. Applications in the food sector
The food sector deals with several complex issues, such as process quality
control, food authenticity and traceability, identification of GMO (genetically
modified organisms). Therefore, NMR technique or isotopic fingerprinting methods,
together with multivariate data analysis, are becoming increasingly important in these
sectors, mainly due to their advantages in terms of cost and efficiency.
2.1. Food authenticity and traceability
Food authenticity and food traceability is of great concern to the consumer,
food processor, retailer and regulatory bodies. For instance, one authenticity issue of
emerging importance is geographic origin, with some selected products permitted to
be marketed using a Protected Designation of Origin (PDO), Protected Geographical
Indication (PGI) or Traditional Specialty Guaranteed (TSG) label on the basis of their
area of production. However, given the financial benefits associated with such labels,
it is very likely that economic frauds occur (e.g. labeling a non-PDO product as a
PDO one).
Isotopic and NMR methods are used for supporting EU policies concerning
the origin of agricultural products. These methods can be used to provide objective
analytical parameters in order to develop a European system for protecting foodstuffs
produced according to certain quality standards such as those of a PDO, PGI or TSG.
In this context, NMR can be used to generate reference fingerprints for these
products. Hence, profiles of suspected counterfeit products can be compared with
these reference data, and fraud be identified.
This system will strengthen the protection of geographical indications and
designations of origin of agricultural products and foodstuffs [Regulation (EEC) No
2081/92], as well as the rules on certificates of specific character for agricultural
products and foodstuffs [Regulation (EEC) No 2082/92]). Furthermore, these methods
will be adapted for control and traceability of organic production methods.
In the EU there is a continuous effort being made for the protection of
products from specific geographical origin, whether applied to wine, cheese or other
goods. Besides food safety and compliance with labeling, the European consumers are
also putting more attention on genuineness and traceability of the food products. This
is especially true for products labeled as “Organic” and “Designated Origin” that
usually sold at higher prices. Therefore, new analytical methods need to be developed
to prove the authenticity of a range of high added value food and beverage products.
Typically, these methods aim to verify the geographic, botanic or varietal origin, the
production process, or the “organic” or “bio” origin of foodstuffs. NMR
fingerprinting methods seem particularly promising in this context, and examples of
applications to coffee,1 tea,2 oils,3-5 fruit juices6 and ciders7 or fish8 can be found in
recent literature. It is worth noting the growing interest of these techniques in the field
of genetically modified organisms (GMO), in the attempt to discriminate between
wild and transgenic plants9,10; or to determine whether the introduction of a gene
induces biochemical changes, or the so called “unintended effects”.11
Many of the previous concerns have been addressed the Food and Quality
Priority of the EU Framework VI research programme. In particular, the TRACE
project was born; (http://www.trace.eu.org), due to the necessity to provide a
‘traceability infrastructure’, that can trace and confirm the origin of a particular
foodstuff. TRACE intends to create a cost-effective system that can identify where
and how foodstuffs are produced, focusing mainly on products labeled “as of
designated origin or organic” for which a combination of the latest methods in
geochemistry, analytical chemistry, statistics, etc. have been applied to identify where
and how foodstuffs were produced. In this context, TRACE will study the relationship
between markers and profiles (isotopic elements, for instance) of a particular food,
and those found in the local environment, i.e., plant and animal tissues. The aim is to
build models based on statistics to develop food maps, indicating the specific
characteristics expected for a given food product coming from a specific area. In this
project, BEVABS in the Joint Research Centre is developing and assessing the NMR
and isotopic fingerprinting tools for instance on olive oils.
In this projects BEVABS has also carried out similar fingerprinting
approaches to the characterization of fish and certain food additives as illustrated
bellow.
Wild or farmed origin of salmon
The salmon market has an important, fast growing role in the economy of the
European Union. According to the Commission Regulation (no. 2065/2001), fish on
sale within the European market should comply with specific labeling regarding the
production method (wild or farmed) and the geographical origin whether farmed or
caught wild. This is also required for all fish products on the market. In this context, a
RTD project (COFAWS: http://www.eurofins.com/research-
development/cofaws/index.asp) was funded by the European Union in order to
develop analytical methods for establishing the geographical origin and the type of
production of salmons.
The application of fingerprinting methods, 13C-IRMS, 15N-IRMS, 1H-NMR
and 13C-NMR, on fish oil and fish muscle samples, together with multivariate
statistical techniques allow the discrimination between wild and farmed salmon
(Figure 1) and the identification of their geographical origin.
Figure 1. LDA-plot obtained with 13C-IRMS, 15N-IRMS, 1H-NMR and 13C-NMR
data of salmon oil and muscle.
Wild or farmed origin of Gilthead Sea Bream (Sparus aurata)
Continuous flow isotope ratio mass spectrometry (CF-IRMS) has been used to
analyze samples of gilthead sea bream (Sparus aurata) of known geographical origin
from wild and farmed sources. δ13C and δ15N values have been measured on muscle
samples as these are the most informative parameters regarding the diet of the fish.
Both of these stable isotopes were indicators of the origin of the fish (Figure 2). The
proposed methodology offers a cost- and time-effective alternative to other analytical
techniques in identifying wild and farmed fish.
Figure 2. Stable isotopes values of δ13C and δ15N in wild and farmed Gilthead Sea
Bream. (Moreno et al., Rapid Comm. Mass Spectrom., 21, 207-211, 2007)
Polyunsaturated Fatty Acids in fish oils: Specie and farming origin
1H-NMR spectroscopy was used to study the polyunsaturated fatty acids
(PUFA) in fish oil from cod and salmon, and how diet as well as the environment in
which the fish was raised affects them. Thus, higher levels of all kinds of unsaturated
fatty acids (UFA) were found in cod oils than in salmon oils (Figure 3A). Moreover,
oils from wild cod tended to present higher levels of DHA, ω-3 and PUFA than those
from farmed cod, but lower levels of UFA, which implies lower levels of
monounsaturated FA.
Regarding wild and farmed salmon, their oils presented completely different
UFA, PUFA, ω-3 and DHA contents (Figure 3B). In general farmed salmon oils
seemed to present lower concentrations of PUFAs than wild (Figure 3C).
The effect of diet on the PUFAs profile of farmed salmon was also studied,
observing that certain diets yielded higher levels of PUFAs in farmed than in wild
samples: F4 and F6 in (Figure 3C). It can also be seen that the ratio DHA to ω-3
PUFAs was affected by the diet (country 4) (Figure 3D).
Regarding the geographical origin of salmons, two distinct PUFA profiles
were observed in farmed salmon (Figure 3E), whereas wild salmon oils did not
present significantly different FA profiles according to their country of origin. In
addition, no seasonal effect was noted on the levels of any kind of UFA in wild or
farmed salmon oils.
Hence, 1H-NMR has proven to be a powerful and versatile tool in this study.
Its potential as an analytical tool for the identification of the fish species, the influence
of diet and farming practices on fish oil, as well as its geographical origin is evident.
So, further exploration into these aspects could prove very interesting both from a
nutritional and an aquaculture perspective.
Country 6Country 1
Arbi
trary
uni
ts
Salmon Cod
0
20
40
60
80
100
120
140
160
Salmon Cod
0
20
40
60
80
100
120
140
160
DHA(A)
Wild Farmed44
46
48
50
52
54
56
58
60
62
64
66
68
70
Wild FarmedWild Farmed44
46
48
50
52
54
56
58
60
62
64
66
68
70
PUFAs(B)
Wild F1 F2 F3 F4 F5 F6240
260
280
300
320
340
360
380
400
420
440
460
480
500
520
Wild F1 F2 F3 F4 F5 F6240
260
280
300
320
340
360
380
400
420
440
460
480
500
520
PUFAs(C)
Country 4
100
120
140
Wild F1 F2
50
60
70
80
100
120
140
Wild F1 F2
100
120
140
Wild F1 F2Wild F1 F2
50
60
70
80
50
60
70
80
ω-3
DHA
(D)
Country 1
Country 2
Country 3
Country 4
Country 5
Country 6
0
20
40
60
80
100
120
140
Country 1
Country 2
Country 3
Country 4
Country 5
Country 6
0
20
40
60
80
100
120
140
(E) PUFAs
Arbi
trary
uni
ts
Country 6Country 1
Arbi
trary
uni
ts
Salmon Cod
0
20
40
60
80
100
120
140
160
Salmon Cod
0
20
40
60
80
100
120
140
160
DHA(A)
Wild Farmed44
46
48
50
52
54
56
58
60
62
64
66
68
70
Wild FarmedWild Farmed44
46
48
50
52
54
56
58
60
62
64
66
68
70
PUFAs(B)
Wild F1 F2 F3 F4 F5 F6240
260
280
300
320
340
360
380
400
420
440
460
480
500
520
Wild F1 F2 F3 F4 F5 F6240
260
280
300
320
340
360
380
400
420
440
460
480
500
520
PUFAs(C)
Country 6Country 1
Arbi
trary
uni
ts
Salmon Cod
0
20
40
60
80
100
120
140
160
Salmon Cod
0
20
40
60
80
100
120
140
160
DHA(A)
Salmon Cod
0
20
40
60
80
100
120
140
160
Salmon Cod
0
20
40
60
80
100
120
140
160
DHA(A)
Wild Farmed44
46
48
50
52
54
56
58
60
62
64
66
68
70
Wild FarmedWild Farmed44
46
48
50
52
54
56
58
60
62
64
66
68
70
PUFAs(B)
Wild Farmed44
46
48
50
52
54
56
58
60
62
64
66
68
70
Wild FarmedWild Farmed44
46
48
50
52
54
56
58
60
62
64
66
68
70
PUFAs(B)
Wild F1 F2 F3 F4 F5 F6240
260
280
300
320
340
360
380
400
420
440
460
480
500
520
Wild F1 F2 F3 F4 F5 F6240
260
280
300
320
340
360
380
400
420
440
460
480
500
520
PUFAs(C)
Wild F1 F2 F3 F4 F5 F6240
260
280
300
320
340
360
380
400
420
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460
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500
520
Wild F1 F2 F3 F4 F5 F6240
260
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480
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520
PUFAs(C)
Country 4
100
120
140
Wild F1 F2
50
60
70
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100
120
140
Wild F1 F2
100
120
140
Wild F1 F2Wild F1 F2
50
60
70
80
50
60
70
80
ω-3
DHA
(D)
Country 1
Country 2
Country 3
Country 4
Country 5
Country 6
0
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140
Country 1
Country 2
Country 3
Country 4
Country 5
Country 6
0
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(E) PUFAs
Arbi
trary
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ts
Country 4
100
120
140
Wild F1 F2
50
60
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120
140
Wild F1 F2
100
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Wild F1 F2Wild F1 F2
50
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50
60
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ω-3
DHA
Country 4
100
120
140
Wild F1 F2
50
60
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140
Wild F1 F2
100
120
140
Wild F1 F2Wild F1 F2
50
60
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50
60
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80
ω-3
DHA
100
120
140
Wild F1 F2
50
60
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Wild F1 F2
100
120
140
Wild F1 F2Wild F1 F2
50
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50
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ω-3
DHA
(D)
Country 1
Country 2
Country 3
Country 4
Country 5
Country 6
0
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140
Country 1
Country 2
Country 3
Country 4
Country 5
Country 6
0
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(E) PUFAs
Country 1
Country 2
Country 3
Country 4
Country 5
Country 6
0
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Country 1
Country 2
Country 3
Country 4
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Country 6
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(E) PUFAs
Arbi
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Figure 3. Polyunsaturated Fatty Acids in salmon and cod oils.
Natural or synthetic origin of tartaric acid
Due to the ever-increasing amount of attention being paid to the ‘naturalness’
of ingredients in food and beverages by both consumers and controlling authorities,
the search for suitable methods for the characterisation of origin is of primary
importance.
Within the European Community the wine production industry is often faced
with the problem of origin control of tartaric acid. This has led to the decision that
only L-tartaric acid extracted from grapes (therefore natural) should be used. In order
to implement these regulations, a screening of different techniques has been carried
out to assess the methodology that best identifies the origin of tartaric acid. It has
already been indicated in scientific literature that isotope ratio mass spectrometry is an
ideal technique for this type of identification. In this study, 13C and 18O stable isotope
ratios are used to discriminate between L-tartaric acids from different sources. The bi-
plot of these isotopes ratios shows a clear discrimination between natural and
synthetic samples (Figure 4).
-33
-31
-29
-27
-25
-23
-21
-19
-170.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00
Synthetic Tartaric AcidNatural Tartaric Acid
δ18O
δ13C
Figure 4. Stable isotopes values of δ13C and δ18O in synthetic and natural tartaric acid.
2.2. Food process quality control
The idea of food control is at the base of the Communication COM(93)360
establishing the European Office for Wine, Alcohol and Spirit Drinks (BEVABS) to
fight major fraud in the wine, alcohol and spirit drinks area. Isotopic analysis (IRMS
and SNIF-NMR) is performed on reference wines and the resulting data are entered
into a European Wine Databank to be used for quality control, data validation and
arbitration of disputes.
In the sector of alcoholic beverages, MAST (DG-JRC, IHCP) has also recently
been asked by DG TAXUD to initiate a NMR fingerprinting study on the analysis and
characterization of alcoholic products (ACAP), with a special attention on “designer
drinks” and on the origin of their ethanol content (fermented, distilled or obtained by
inverse osmosis), for the resolution of the issue of divergent classifications of
alcoholic beverages for excise duties.
Figure 5. Examples of 1H NMR fingerprints of alcoholic beverages.
Examples of the (potential) application of these analytical techniques to
quality control in food processing include the production of beers,12-15 coffee,1
ginseng preparations16 or fruit juices.17,18 In the case of beers, it has been shown that
multivariate analysis of 1H NMR spectra could be used to discriminate beers made
from barley or wheat malt13, ales and lagers (reflecting the fermentation type),14,15 and
between production sites.12,13 Furthermore, beers with quality defects (spoiled beers)
can be identified.13 These techniques have also been tested to study the effects of
cultivar, climate, soil and cultural practises (“terroir”) on the grapes used for
winemaking.19
Austrian white wine
1.52.02.53.03.54.04.55.05.56.06.57.07.58.08.5 ppm
Glycerol
Tuborg beer
Bacardi Breezer orange
Glycerol
Glycerol
EtOH EtOH
EtOH
EtOHEtOH
EtOHHDO
HDO
HDO
Lactate
AlanineAcetate
Sucrose Sucroseβ-Glucose Sugars
Citrate
Maltose
MaltoseMaltose
Maltose + Dextrins Polyphenols
3. A case study: Characterization of PDO olive oils by NMR and IRMS
Olive oil is of significant importance as a high added value agricultural
product for the European Union, in both commercial and nutritional terms. Spain,
Italy and Greece, account for 79% of the world production and 71% of the world
consumption. PDO olive oils are sometimes subject to adulteration with olive oils that
do not fulfill the PDO requirements. For this reason validated methods which allow us
to guarantee the authenticity and traceability of PDO olive oils are essential. The
authentication of olive oils with respect to their geographical, botanical and varietal
origin has been studied using various analytical approaches: NMR (1H, 13C, 31P), NIR
spectroscopy, IRMS, LC-MS, GC-MS3, 20-22. However, most of these studies
considered a limited number of samples and geographical areas.
As a TRACE partner and contributing to its goal, BEVABS is carrying out
further research on extra virgin olive oils. The aim of this study is to enable the
geographical characterization of olive oils by NMR and IRMS. For this purpose,
BEVABS in collaboration with other partners (TRACE project and scientific
contacts) has collected a statistically significant number of authentic PDO extra-virgin
olive oils from EU and non EU countries (716 samples) during two seasons, 2005 and
2006. In 2005, we collected olive oils from Italy (226 (63 from Liguria), Spain (72),
Greece (43), Turkey (14) and France (9); whereas in 2006 samples were from Italy
(252 (79 from Liguria), Spain (38), Greece (46), France (10) and Cyprus (6). The
Italian samples were representative of the olive oil producing areas, which are
markedly influenced by the different climatic and environmental factors from the
North to the South of the country.
[ppm]6 5 4 3 2 1 [ppm]6 5 4 3 2 1 7 [ppm]6 5 4 3 2 1 [ppm]6 5 4 3 2 1 7 [ppm]6 5 4 3 2 1 [ppm]6 5 4 3 2 1 7
[ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4
[ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4
[ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5
[ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2
[ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7
[ppm]6 5 4 3 2 1 [ppm]6 5 4 3 2 1 7 [ppm]6 5 4 3 2 1 [ppm]6 5 4 3 2 1 7 [ppm]6 5 4 3 2 1 [ppm]6 5 4 3 2 1 7
[ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4
[ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4
[ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5
[ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2
[ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30 [ppm]5.45 5.40 5.35 5.30
[ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]4.4 4.3 4.2 4.1 4.0 3.9 3.8 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4 [ppm]2.9 2.8 2.7 2.6 2.5 2.4
[ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4 [ppm]1.2 1.0 0.8 0.6 0.4
[ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5 [ppm]5.0 4.9 4.8 4.7 4.6 4.5
[ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2 [ppm]1.5 1.4 1.3 1.2
[ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7 [ppm]2.2 2.1 2.0 1.9 1.8 1.7
Chemical shifts Compound Chemical shifts (ppm)Carbon
2.74 – 2.90=CH-CH2-CH=Linoleic and linolenic acid 2.74 – 2.90=CH-CH2-CH=GlycerylGlycerylGlyceryl
4.10 – 4.194.26 – 4.335.24 – 5.285.30 – 5.44
C1,3 protonsC1,3 protonsC2 proton-CH=CH-
GlycerylGlycerylGlycerylAll unsaturated FA
4.10 – 4.194.26 – 4.335.24 – 5.285.30 – 5.44
C1,3 protonsC1,3 protonsC2 proton-CH=CH-
0.85 - 0.910.95 – 1.001.20 – 1.401.611.97 – 2.122.28 – 2.34
-CH3
-CH3
-(CH2)n--(CH2)n-CH2--(CH2)n-CH=CH-CH2-
0.85 - 0.910.95 – 1.001.20 – 1.401.611.97 – 2.122.28 – 2.34
-CH3
-CH3
-(CH2)n--(CH2)n-CH2-COOR-(CH2)n-CH=CH-CH2-COOR
All acyl chains, except linolenic
All unsaturated FA
Linolenic acidAll acyl chainsAll acyl chains
All acyl chains
Chemical shifts Compound Chemical shifts (ppm)Carbon
2.74 – 2.90=CH-CH2-CH=Linoleic and linolenic acid 2.74 – 2.90=CH-CH2-CH=GlycerylGlycerylGlyceryl
4.10 – 4.194.26 – 4.335.24 – 5.285.30 – 5.44
C1,3 protonsC1,3 protonsC2 proton-CH=CH-
GlycerylGlycerylGlycerylAll unsaturated FA
4.10 – 4.194.26 – 4.335.24 – 5.285.30 – 5.44
C1,3 protonsC1,3 protonsC2 proton-CH=CH-
0.85 - 0.910.95 – 1.001.20 – 1.401.611.97 – 2.122.28 – 2.34
-CH3
-CH3
-(CH2)n--(CH2)n-CH2--(CH2)n-CH=CH-CH2-
0.85 - 0.910.95 – 1.001.20 – 1.401.611.97 – 2.122.28 – 2.34
-CH3
-CH3
-(CH2)n--(CH2)n-CH2-COOR-(CH2)n-CH=CH-CH2-COOR
All acyl chains, except linolenic
All unsaturated FA
Linolenic acidAll acyl chainsAll acyl chains
All acyl chains2.74 – 2.90=CH-CH2-CH=Linoleic and linolenic acid 2.74 – 2.90=CH-CH2-CH= 2.74 – 2.90=CH-CH2-CH=Linoleic and linolenic acid 2.74 – 2.90=CH-CH2-CH=
GlycerylGlycerylGlyceryl
4.10 – 4.194.26 – 4.335.24 – 5.285.30 – 5.44
C1,3 protonsC1,3 protonsC2 proton-CH=CH-
GlycerylGlycerylGlycerylAll unsaturated FA
4.10 – 4.194.26 – 4.335.24 – 5.285.30 – 5.44
C1,3 protonsC1,3 protonsC2 proton-CH=CH-
GlycerylGlycerylGlyceryl
4.10 – 4.194.26 – 4.335.24 – 5.285.30 – 5.44
C1,3 protonsC1,3 protonsC2 proton-CH=CH-
GlycerylGlycerylGlycerylAll unsaturated FA
4.10 – 4.194.26 – 4.335.24 – 5.285.30 – 5.44
C1,3 protonsC1,3 protonsC2 proton-CH=CH-
0.85 - 0.910.95 – 1.001.20 – 1.401.611.97 – 2.122.28 – 2.34
-CH3
-CH3
-(CH2)n--(CH2)n-CH2--(CH2)n-CH=CH-CH2-
0.85 - 0.910.95 – 1.001.20 – 1.401.611.97 – 2.122.28 – 2.34
-CH3
-CH3
-(CH2)n--(CH2)n-CH2-COOR-(CH2)n-CH=CH-CH2-COOR
All acyl chains, except linolenic
All unsaturated FA
Linolenic acidAll acyl chainsAll acyl chains
All acyl chains
0.85 - 0.910.95 – 1.001.20 – 1.401.611.97 – 2.122.28 – 2.34
-CH3
-CH3
-(CH2)n--(CH2)n-CH2--(CH2)n-CH=CH-CH2-
0.85 - 0.910.95 – 1.001.20 – 1.401.611.97 – 2.122.28 – 2.34
-CH3
-CH3
-(CH2)n--(CH2)n-CH2-COOR-(CH2)n-CH=CH-CH2-COOR
0.85 - 0.910.95 – 1.001.20 – 1.401.611.97 – 2.122.28 – 2.34
-CH3
-CH3
-(CH2)n--(CH2)n-CH2--(CH2)n-CH=CH-CH2-
0.85 - 0.910.95 – 1.001.20 – 1.401.611.97 – 2.122.28 – 2.34
-CH3
-CH3
-(CH2)n--(CH2)n-CH2-COOR-(CH2)n-CH=CH-CH2-COOR
All acyl chains, except linolenic
All unsaturated FA
Linolenic acidAll acyl chainsAll acyl chains
All acyl chains
All acyl chains, except linolenic
All unsaturated FA
Linolenic acidAll acyl chainsAll acyl chains
All acyl chains
Figure 6. 1H-NMR spectrum of an olive oil and the chemical shifts of the main
signals.
The 1H-NMR and 1H and 13C-IRMS fingerprints of these PDO olive oils were
analysed by exploratory data analysis techniques, such as Principal Component
Analysis (PCA); and supervised pattern recognition techniques, such as Linear
Discriminant Analysis (LDA) and Partial Least Squares Discriminant Analysis (PLS-
DA), in order to identify the geographical origin of PDO olive oils at different levels
such as the country and the region levels, as well as to obtain binary classifications
regarding the olive oils as belonging or not belonging to a certain PDO or region (the
goal of the TRACE project).
NMR spectra of olive oils were recorded, processed (Fourier transform, phase
and baseline corrections, calibration) and integrated (buckets of 0.02 ppm), resulting
in a data sets with 342 variables (buckets). In Figure 6, the NMR spectrum of an olive
oil is shown and the main signal listed in the table below.
The models obtained by the pattern recognition techniques were validated by
cross-validation, and evaluated by their recognition (the percentage of the samples in
the training set correctly classified) and prediction (the percentage of the samples in
the test set correctly classified) abilities. For LDA, a variable selection procedure,
consisting of modified best subset selection and forward stepwise selection, was
performed previous to the modeling step. Whereas, PLS-DA processing was applied
on the whole NMR spectra.
Influence of the year of production on the PDO olive oils
Taking the 1H-NMR data, a seasonal influence of the year of production of
olive oils is observed in the bidimensional plot obtained by PCA (Figure 7), the two
groups of olive oils, being partially overlapped. This is probably due to
environmental, agricultural (olive trees production alternates: one year it is high, the
next it is low) and climatic factors affecting the olive cultivars, which certainly can
vary between seasons.
Regarding the origin of the olive oil, 70% of the samples were Italian, and the
other 30% from countries in the same Mediterranean region. It would therefore seem
that seasonal aspects affected all samples in the same way regardless of the country of
origin. In the same way, Ligurian and non-Ligurian olive oils from both seasons
overlapped. Hence, the effect of seasonal variability is also included in the data.
-3 -2 -1 0 1 2 3
PC 1 (31.8% of total variance)
-3
-2
-1
0
1
2
3
PC 3
(13.
2% o
f tot
al v
aria
nce)
Year 2005Year 2006
-3 -2 -1 0 1 2 3
PC 1 (31.8% of total variance)
-3
-2
-1
0
1
2
3
PC 3
(13.
2% o
f tot
al v
aria
nce)
-3 -2 -1 0 1 2 3
PC 1 (31.8% of total variance)
-3
-2
-1
0
1
2
3
PC 3
(13.
2% o
f tot
al v
aria
nce)
Year 2005Year 2006Year 2005Year 2006Year 2005Year 2006Year 2005Year 2006
Figure 7. PCA applied to 1H-NMR data of olive oils collected in two years, 2005 and
2006.
PDO olive oils classification by country
Olive oils (671 samples) coming from the main producing countries, i.e., Italy
(473), Spain (110) and Greece (88), and collected in both years, were analysed by 1H-
NMR. Their 1H-NMR spectra (342 variables) were submitted to LDA and PLS-DA,
obtaining the classification results presented in tables 1 and 2 and Figure 8.
.
LDA (19 var) Recognition ability (%) Prediction ability (%)
Italy 96.7 95.3
Spain 76.4 70.9
Greece 86.9 85.2
Table 1. Recognition and prediction abilities for the classification of olive oils
according to the country of origin, obtained by LDA
PLS-DA Recognition ability (%) Prediction ability (%)
Italy 97.9 95.1
Spain 78.6 70.9
Greece 87.5 76.1
Table 2. Recognition and prediction abilities for the classification of olive oils
according to the country of origin, obtained by PLS-DA.
Italy Spain Greece-4 -2 0 2 4 6 8
Root 1
-8
-6
-4
-2
0
2
4
6
Roo
t 2
Italy Spain Greece
Italy Spain Greece-4 -2 0 2 4 6 8-4 -2 0 2 4 6 8
Root 1
-8
-6
-4
-2
0
2
4
6
Roo
t 2
Figure 8. LDA applied to 1H-NMR data of Italian, Spanish and Greek olive oils.
The results obtained by both multivariate techniques are similar, this fact
implies that the results are reliable. More than 95% of Italian olive oils are classified
correctly. However, these results are biased to a certain extent, due to the unbalanced
number of samples in each class; there were four times more Italian samples than the
other countries. Despite of this drawback, the classifications for Greek and Spanish
olive oils are promising, once the classes are equilibrated.
Furthermore, the olive oils from 2005 were analyzed for isotopic D/H and 13C/12C ratios by IRMS. The information provided by these technique was added to
that provided by 1H-NMR.
LDA (14 NMR var) PLS-DA Recognition ability (%) NMR NMR+IRMS (13C) NMR NMR+IRMS (2H + 13C)
Italy 96.4 96.9 99.3 99.3
Spain 83.3 85.4 91.5 91.5
Greece 87.2 90.7 100.0 100.0
Prediction ability (%)
Italy 94.7 94.7 96.0 95.1
Spain 78.3 84.1 83.1 87.3
Greece 81.4 88.4 74.4 95.3
Table 3. Recognition and prediction abilities for the classification of olive oils
according to the country of origin, obtained by LDA and PLS-DA.
In LDA, the isotopic ratio D/H was not significant for the classification of the
olive oils according to their country of origin, whereas the 14 NMR buckets together
with the isotopic ratio 13C/12C achieved better results than NMR data alone (Table 3).
However, the best classifications were obtained by PLS-DA, which uses the whole 1H-NMR spectrum and both isotopic measurements. With this technique, Greek olive
oils are perfectly recognized by the classification model; and over 99% of Italian
samples, also the prediction abilities for both countries are higher than 95%. The PLS-
DA model for Spanish olive oils obtained considerably better classifications (91% and
87% of recognition and prediction abilities, respectively) than the LDA model.
Italian PDO olive oils classification by region
Italian PDO olive oils (225 samples) from 2005 were analyzed by 1H-NMR and
IRMS, and LDA was used for the multivariate analysis of the data. The best results
were achieved when both isotopic ratios, D/H and 13C/12C, were used in combination
with the selected NMR variables (Table 4). Due to the unbalanced number of samples
in each class, the classification abilities of the model for the regions with a lower
number of samples, i.e. Molise & Abruzzo, Campagna and Calabria were around 50%
or less. In contrast, Liguria and Lazio & Umbria presented recognition and prediction
abilities higher than 90%. Regarding the regions of Lago di Garda, Puglia and Sicilia,
encouraging results were achieved (more than 70% of correct classifications), taking
into account the unbalanced number of samples in these classes.
Recognition ability (%) Prediction ability (%) Italian regions n NMR (7 var) NMR + IRMS NMR (6 var) NMR + IRMS
Lago di Garda 18 52.8 80.6 33.3 72.2
Liguria 62 98.4 98.4 98.4 96.8
Molise & Abruzzo 19 23.7 52.6 15.8 31.6
Lazio & Umbria 47 86.2 92.6 80.9 93.6
Campagna 7 71.4 100.0 71.4 57.1
Puglia 28 73.2 83.9 71.4 78.6
Calabria 13 50.0 57.7 38.5 53.8
Sicilia 31 71.0 75.8 71.0 74.2
Table 4. Recognition and prediction abilities for the classification of Italian olive oils
according to the region of origin, obtained by LDA and PLS-DA.
Binary classification of olive oils related to the membership to a certain PDO:
“Rivera Ligure”
1H-NMR spectra and the isotopic D/H and 13C/12C ratios of the PDO olive oils
from 2005 were analyzed by LDA and PLS-DA in order to differentiate olive oils
from a certain PDO, e.g. the Italian PDO “Rivera Ligure”, from other olive oils not
belonging to this PDO.
LDA (5 NMR var) Recognition ability (%) Prediction ability (%) n NMR NMR + IRMS NMR NMR + IRMS
Ligurian 61 71.3 75.4 67.1 73.8
Non-Ligurian 252 96.0 98.4 95.5 98.0
Table 5. Recognition and prediction abilities for the classification of oils as Ligurian
or non-Ligurian by LDA.
PLS-DA Recognition ability (%) Prediction ability (%) n NMR NMR + IRMS NMR NMR + IRMS
Ligurian 61 90.2 93.4 88.5 90.2
Non-Ligurian 252 99.8 100.0 98.8 99.2
Table 6. Recognition and prediction abilities for the classification of oils
as Ligurian or non-Ligurian by PLS-DA.
LigurianNon-Ligurian
x scores 1
x sc
ores
2
x sc
ores
3
LigurianNon-LigurianLigurianNon-LigurianLigurianNon-LigurianLigurianNon-Ligurian
x scores 1
x sc
ores
2
x sc
ores
3
Figure 9. PLS-DA applied to 1H-NMR data and isotopic D/H and 13C/12C ratios of
Ligurian and non-Ligurian olive oils.
Despite the unbalanced number of samples in each class, satisfactory
classification results were achieved, in particular by PLS-DA, which identified 99%
of the olive oils which did not belong to the Ligurian PDO as non-Ligurian, whereas
only 10% of the Ligurian PDO olive oils were misclassified as not belonging to the
PDO (Tables 5 and 6, Figure 9).
1H-NMR of the unsaponifiable fraction of olive oils for the determination of
geographical origin
Different approaches are being studied in our laboratory for the determination
of the geographical origin of PDO extra-virgin olive oils. This is a new approach
based on 1H-NMR analysis of the unsaponifiable fraction of olive oils is presented.
The unsaponifiable fraction is obtained by a standard procedure, dissolved in
deuterated chloroform and analysed by 1H-NMR. Multivariate data analysis of
preliminary results shows that the present approach demonstrates potential for the
geographical characterization of olive oils (Figure 10 and Table 7). Olive oils from
Turkey and Tunisia are all correctly classified by the model. For the Italian class, the
model also presents satisfactory recognition and prediction abilities, 98% and 91%
respectively. The large differences between the recognition and prediction abilities
indicate that the results depend on the samples in the training and test set, and
therefore, the results present a certain instability for these two classes. This may be
overcome by increasing and equilibrating the number of samples in each class.
LDA (11 NMR var) ability (%) Country n Recognition Prediction
Italy 21 97.6 90.5
Spain 15 86.7 66.7
Greece 15 90.0 80.0
Turkey 7 100.0 100.0
Tunisia 19 100.0 100.0
Table 7. Recognition and prediction abilities for the classification of the
unsaponifiable fraction of olive oils according to the country of origin,
obtained by LDA.
Italy Spain Greece Turkey Tunisia
Italy Spain Greece Turkey Tunisia
Italy Spain Greece Turkey Tunisia
Italy Spain Greece Turkey Tunisia
Figure 10. LDA applied to 1H-NMR of the unsaponifiable fractions of olive oils from
Italy, Spain, Greece, Turkey and Tunisia.
Conclusions
1H-NMR spectra of olive oils contain useful information for the classification
of olive oils according to their geographical origin. However, the addition of the
information provided by the IRMS isotopic measurements, i.e. D/H and 13C/12C
ratios, to the 1H-NMR data considerably improves the classification results of olive
oils.
Better classification results are expected by improving data processing,
increasing the number of samples with also more balanced cases for each class or
origin and collecting data over several seasons.
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European Commission
EUR 22724 EN– DG Joint Research Centre, Institute for Health and Consumer Protection
NMR And Isotopic Fingerprinting For Food Characterisation
Authors: MORENO ROJAS JOSE', ALONSO SALCES ROSA, HOLLAND MARGARET, RENIERO FABIANO, GUILLOU
CLAUDE, SERRA FRANCESCA, SEGEBARTH NICOLAS
Luxembourg: Office for Official Publications of the European Communities 2007 – 23 – 21 x 29.7 cm EUR - Scientific and Technical Research series; ISSN 1018-5593 ISBN 978-92-79-05309-2 Abstract Numerous analytical methods have been developed during the past decades and have proven to be extremely efficient, for instance, in the case of single, high purity compounds for the measurements of concentration and/or structure elucidation. However, real-world applications often require the characterization of complex mixtures containing tens to thousands of compounds, such as biofluids, food matrices, industrial products, etc. The complete characterisation of such mixtures would be tedious, not to say impossible in the case of mixtures containing hundreds of compounds, and certainly unfeasible for monitoring purposes. In fact, one can concentrate on one or a few molecules which entail the non-negligible issue of the choice of the molecules of interest, and therefore require an a priori knowledge. Nevertheless this approach usually requires molecular separation and purification, which is time, money and human resource consuming. In contrast the Nuclear Magnetic Resonance (NMR) fingerprinting aims at establishing a holistic approach: the mixture issubmitted to the NMR experiment as a whole. A simple quantification of the major compounds, which are characterised byone or several signals in the NMR spectrum, can be performed. This type of analysis is particularly attractive for several reasons: it is non-destructive, non selective and cost effective; requires little or no sample pre-treatment; uses small amounts of organic solvents or reagents; and typically takes only a few minutes per sample.The spectra of complex mixtures show hundreds of signals, coming from numerous molecules. This and the overlap of signal make it difficult to extract information, either visually or by simple processing of the data. The most effective way to analyse these holistic profiles is by using chemometric tools which enable the visualisation of the data in a reduced dimension and the classification of the samples into established classes based on inherent patterns in a set of spectral measurements.Moreover, these techniques also allow to trace the NMR spectral variables responsible of this classification, and thus, identify molecular markers of interest. Isotopic measurements such as Isotopic Ratio Mass Spectroscopy (IRMS) or Site-specific Natural Isotopic Fractionation (SNIF-NMR) provide few variables, but these contain unique information on geographical origin and metabolic or production pathways. Thus, isotopic measurements provide complementary data to NMR fingerprinting.
